class_docstr: A class representing a discrete probability integral transform of a given
  quantile-based learned binning.  Note that this representation is intended
  to be applied on top of a quantile-based binning representation.
  
  Parameters
  ----------
  num_bins
      Number of bins used by the data on which this representation is
      applied.
  mlp_tranf
      Whether we want to post-process the pit-transformed valued using a MLP
      which can learn an appropriate binning, which would ensure that pit
      models have the same expressiveness as standard quantile binning with
      embedding.
      (default: False)
  embedding_size
      The desired layer output size if mlp_tranf=True. By default, the
      following heuristic is used:
      https://developers.googleblog.com/2017/11/introducing-tensorflow-feature-columns.html
      (default: round(num_bins**(1/4)))
code: def __init__(
      self,
      num_bins: int,
      mlp_transf: bool = False,
      embedding_size: Optional[int] = None,
      *args,
      **kwargs,
  ):
      super().__init__(*args, **kwargs)
  
      self.num_bins = num_bins
      self.mlp_transf = mlp_transf
  
      if embedding_size is None:
          self.embedding_size = round(self.num_bins ** (1 / 4))
      else:
          self.embedding_size = embedding_size
  
      if mlp_transf:
          self.mlp = nn.HybridSequential()
          self.mlp.add(
              nn.Dense(
                  units=self.num_bins, activation="relu", flatten=False
              )
          )
          self.mlp.add(
              nn.Dense(units=self.embedding_size, flatten=False)
          )
      else:
          self.mlp = None
  
file_path: gluonts/src/gluonts/mx/representation/discrete_pit.py
imports: from typing import List, Optional, Tuple
  from mxnet.gluon import nn
  from gluonts.core.component import validated
  from gluonts.mx import Tensor
  from .representation import Representation
label_desc: Use image node_id 1 to create a new DiscretePIT object from inherited base classes: Representation with example: obj = DiscretePIT(num_bins, mlp_transf, embedding_size)